A Weakly Supervised Multi-task Ranking Framework for Actor–Action Semantic Segmentation

Modeling human behaviors and activity patterns has attracted significant research interest in recent years. In order to accurately model human behaviors, we need to perform fine-grained human activity understanding in videos. Fine-grained activity understanding in videos has attracted considerable recent attention with a shift from action classification to detailed actor and action understanding that provides compelling results for perceptual needs of cutting-edge autonomous systems. However, current methods for detailed understanding of actor and action have significant limitations: they require large amounts of finely labeled data, and they fail to capture any internal relationship among actors and actions. To address these issues, in this paper, we propose a novel Schatten p-norm robust multi-task ranking model for weakly-supervised actor–action segmentation where only video-level tags are given for training samples. Our model is able to share useful information among different actors and actions while learning a ranking matrix to select representative supervoxels for actors and actions respectively. Final segmentation results are generated by a conditional random field that considers various ranking scores for video parts. Extensive experimental results on both the actor–action dataset and the Youtube-objects dataset demonstrate that the proposed approach outperforms the state-of-the-art weakly supervised methods and performs as well as the top-performing fully supervised method.